Let’s Talk Basketball: MVP Score

I decided to do a sort of scoring method for this based on seven categories, most of them objective, one of them not (which can be a source of discussion). But in the end, we want to place a concrete value for ideas that normally come into play for “MVP.”

The Scoring System

First, an MVP (or in this case, BPC or BIC) should be a decent offensive weapon. MVPs are normally born out of players who contribute a lot to their teams’ offensive production. This plays a large role in terms of who the MVP should be particularly because offense is a more individualistic process as compared to defense and thus easier to dissect. For our case, we use a player’s ORTG and USG%. ORTG is an offensive metric developed by Dean Oliver that aims to take into account all the important, individual box score metrics that relate to offense: field goals (whether it’s a three or a two, a miss or a make), assists, offensive rebounds, free throws and turnovers. USG% is a measure of how many possessions a player used while he’s on the court. Since there are five players on the court at any given time, the normal USG% would be 20% (i.e. it’s equally divided among the five members).

Second, I’d like to think that MVPs should come from teams that have won at least 50% of their games and the said player played a good percentage of the time. I pegged it at 60% (i.e. the player’s total minutes divided by his team’s total minutes on games he played was at least 60%). We want players that are high efficiency, high usage players. That’s the goal. We assign 30 points to ORTG and 20 points to USG.

The player must also play a lot of games with the team. It’s pretty useless if a player was a high usage and high efficiency player but only played just two games. His effect would not be felt as much as a player who played say 10 games or 14 games. We’ll therefore divide a player’s total minutes played by the team’s total minutes played. Why not the team’s total minutes played while the player was on the court? Because we want to see how much a player played. So Cabagnot, who missed 3 games, played in just 47.4% of his team’s minutes. Compare that to LA Tenorio, who did not miss a single game, and played 38 minutes per game, that equates to 79.2% of his team’s minutes played. Clearly, Tenorio had a larger effect on the team’s conference than Cabagnot. We’ll assign 10 points to this.

Third, we also want a player who produces a lot. (Outside of those that relate to offense. i.e. field goals, assists, offensive rebounds, turnovers.) Thus, we’ll take the PER or Player Efficiency Rating from John Hollinger. PER is a statistic that takes into account all the player’s positive contributions (made field goals, threes made, assists, offensive rebounds, defensive rebounds, steals, blocks, etc.) and deducts a player’s negative contributions (missed field goals, turnovers, fouls committed, missed free throws, etc.) and adjusts for his team’s pace (number of possessions used by the team per game) and his minutes played. We’ll also assign 30 points to this.

Fourth, aplayer should also be awarded for his team’s wins. If a player is productive and his team is winning, then he should be awarded for this, right? We’ll assign 25 points to this.

Fifth, we also want to see how a team performs with a player not on the team. This is an argument that people use to name a MVP (see Gary Washburn’s argument from the Boston Globe for Carmelo Anthony of the New York Knicks). To some extent, there is merit to this. A player SHOULD be rewarded for carrying a team that is not that talented. We’ll give this 20 points.

Finally, the subjective part, we’ll add an “intangibles” score – this is a score I gave (not something that the numbers indicate but is based on what I saw) to the player’s defensive contributions (which if you notice, are absent from the analysis) and a player’s leadership abilities. I’ll be giving this part a score of 10.

Here is the final tally:

Statistic

Weight

Offensive Rating

30

Player Efficiency Rating

30

W/L%

25

Estimated on/off

20

Usage Rate

20

% on the court

10

Intangibles + Defense

10

TOTAL

145

This is exactly the way people usually see it: player’s productivity first (ORTG, PER) then W/L, then a player’s effect on a team (USG% and on/off) then a player’s other peripherals (% on the court, Intangibles + Defense). This is the part that can be debated (weights for each) but by and large, I think this accurately encompasses how people usually view a “best in basketball” conversation.

How do we score this? Well for everything outside of PER and Intangibles, we award a perfect score to the player with the best in the category. Afterwards, the score of the other players will be based on how far off the player is from the top. Example, if Player A has an ORTG of 110 (and this was the best in the group), then he’ll get a perfect 30. Player B has an ORTG of 101 and is 9 points off the top (in this case, 110). Which means his ORTG is 9/110 = 8% worse than the best player. So he’ll get a deduction of 8% on his score, which means his score in the ORTG column is 30 * (100% – 8%) = 27.6.

For PER and Intangibles, we simply add it to the total. I think the intangibles part is easy to understand (since I just gave subjective scores with a maximum of 10). The PER will simply be added because getting a PER of 30 (which is the maximum score we give for PER) is already INSANE (which means he deserves a perfect score just by getting there). A PER of over 30 means he’ll be getting extra points from the system (like in the case of Eric Dawson, who registered a PER of 30.7).

The MVP score will therefore be a summation of all 7 numbers, divided by 145 (the perfect score) and then multiplied by 100. This means that the MVP score’s perfect card is 100.

Warning: This is a very mathematical part of the exercise. If you do not wish to know the details of the estimation, please skip this part. I’ve placed broken lines to indicate the estimation part. Click here to jump to the results.

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The Estimation

Now, how do we estimate the on/off court? Long story short, you subtract a player’s offensive box score to the team’s offensive box scores while he was on the court. I’ll use an example to make things easier. Here is Robert Dozier’s stats (his and the team’s stats while he was on the court).

Points

FGM

FGA

3PTM

2PTM

FTM

FTA

AST

ORB

TO

Robert Dozier

304

103

201

3

100

95

146

32

73

35

Alaska (w/ Dozier)

1192

410

1017

68

342

304

425

242

195

212

As additional info, the total defensive rebounds by Alaska opponents while Dozier was on the court is 508.

All right, we begin by first calculating the raw ORTG. This is different from the one given in the PBA Commissioner’s Cup Complete Stats by Humblebola. Those are adjusted for the opponent team’s possessions since each team should have more or less the same amount of possessions in each game. No adjustment is done here.

Alaska’s raw possession count:

Substituting the numbers we have:

Simplifying everything:

If we divide 1192 (Alaska’s total points scored) by 1251.9, we come up with Alaska’s raw ORTG = 95.2 points per 100-possession.

Now, the logic behind the estimated on/off ORTG is this: if we take away all the player’s points scored, field goals (made or attempted), free throws (made or attempted), offensive rebounds and turnovers, what will the resulting ORTG be for the team?

To further explain this, if a team scored 89 points via 92 possessions (for an ORTG of 96.7 points per 100-possession) and player A scored 20 points on approximately 15 possessions, then his team would score only 69 points on 77 possessions (for an ORTG of 89.6 points per 100-possession). This means his team is 7.1 points WORSE when he’s not contributing ON the court. Kapeesh?

Team ORTG has five key components: points scored, field goals attempted, offensive rebounds (or offensive rebounding percentage), field goals attempted, free throws attempted and turnovers. The only place we’ll have a problem is in “points scored.” Everything else, we can just simply deduct the player’s stats to the team’s stats when he played. The problem with points scored comes mainly from the value of an individual assist. Assists are valued within a points scored. What does this mean? When a player scores two unassisted points, the entire two points should be credited to him. But when a player scores two assisted points, part of that sum should go to the assister (since he, in theory, did help the shooting player get those two points). Most of the known analysts peg the value of an assist at around one third (or 33%). It can be higher or lower but this is a generally accepted value for an assist. I’ll hope to unlock (someday) the true value of an assist, but for now? This will have to do.

This means that if a player scores two assisted points, only 1.4 should be credited to him, the rest (or 0.6 of the points) should be credited to the assister.

Now that it is out of the way, how do we estimate how many of a player’s field goals made were assisted and the distribution of the player’s assists (since threes are more valuable than twos). First, we adjust all relevant stats based on how many minutes the player played in. In Dozier’s case, since he played a total of 579 minutes and the team’s total available minutes when he played was 677, then we adjust everything by

So instead of using 410 field goals made for Alaska, we use 351 (or 350.55) field goals made. This is an estimate of how many field goals made were taken while Dozier was on the court. Of course this is not exact (the behavior of a team might change when a certain player comes in; they may play faster, shoot more and rebound less) but that is an inherent limitation of this estimate. We do this for field goals made, 3PT made, 2PT made and assists (all of the relevant statistics as it pertains to the calculation of the value of an assist).

To calculate the player’s Assisted % (i.e. how many of his field goals made were assisted), here is the formula:

The numerator is simple enough: it is the total number of assists that were made by his teammates. If we divide it by the total field goals made, then that is the ratio of the assists made by teammates to field goals made. Again, there are inherent problems but that is part of the study. I am recognizing it as a weakness.

Now that we have that, we need to get the distribution of his assists (how many were 3PT makes and how many were 2PT makes). This is the formula:

We do the same for 2PT (just replace 3PTM with 2PTM).

In a sense, we got all the 3PT makes by his teammates and then divided it by the total field goals made by his teammates. Our assumption here is that the distribution of assists follow the distribution of his teammates shots. This isn’t necessarily the case (since some players get more assists out of 3PT shots). Nonetheless, this is an assumption we’ll make. Using Rob Dozier as an example, I estimated that 22% of Dozier’s assists result into 3PT shots, the other 77% result into 2PT shots. If we give Dozier 1 point (or 33% of 3) for each assist resulting into 3PT makes and 0.6 (or 33% of 2) for each assist resulting into 2PT makes. Then, since Dozier has 32 assists, he assisted on ~7 3PT makes (or 32*20%) and ~25 2PT makes (or 32*80%). His points produced from his assists is therefore 7 + (0.6*25) = 22.

Similarly, we do this for Dozier’s FGM. Since we have an estimate of Dozier’s Assisted% (which is for Dozier’s case, 50%) then this means 50% of Dozier’s 3 3PTM (or 1.5) will only get a credit of 2 (since the other 1 points went to the player that assisted him). Also, 50% of Dozier’s 100 2PTM (or 50) will only get a credit of 1.4 (since the other 0.6 went to the player that assisted him). The other 50% for both? Full credit. In terms of an equation, thus the four parts of Dozier’s points produced from field goals made is:

1.5 * 2 (this is the assisted 3PT part) = 3

1.5 * 3 (this is the unassisted 3PT part) = 4.5

50 * 1.4 (this is the assisted 2PT part) = 70

50 * 2 (this is the unassisted 2PT part) = 100

Add all five up (including the points produced from assist) and Dozier’s total free throw makes and we get Dozier’s total points produced which is simply 294.6 (some rounding error exists). This is the number that we will subtract to the team’s points scored. Now we just subtract everything and calculate the new ORTG (without the player’s contribution).

Points scored = 1192 – 294.6 = 897.4

FGM = 410 – 103 = 307

FGA = 1017 – 201 = 816

FTA = 425 – 146 = 279

ORB = 195 – 73 = 122

Opp DRB (we just adjust this by the minutes) = 508 * 85.5% = 434.34

TO = 212 – 35 = 177

We get the total possessions from this group.

Alaska’s raw possession count (w/o Dozier):

Simplifying everything:

We divide the total points scored (897.4) by 1006.9 and we get 89.1 points per 100-possession. This means Alaska is (95.2 – 89.1) = 6.1 points better when Dozier is ON the court.

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The Results

Lastly, players who should be subjected to this discussion should be players that are worthy of being included. This means they should have played at least 10 games with at least 28 minutes per game, come from the 7 teams with at least a .500 record (Alaska, Rain or Shine, Petron, San Mig Coffee, Talk ‘n Text, Meralco and Ginebra), have an ORTG that is above the league average (96.07) and, for locals, should have a USG% of at least 15%. In reality, this group consists of four imports and six locals. namely: Robert Dozier, Vernon Macklin, Eric Dawson, Bruno Sundov, LA Tenorio, Alex Cabagnot, Marcio Lassiter, PJ Simon, Jimmy Alapag and Larry Fonacier.

As an FYI, Castro did not meet the minutes requirement (25.7 MPG) and Abueva did not meet the minutes and ORTG requirement (26.2 MPG, 86.8 ORTG). However, I did add one name into the discussion despite not reaching the requirements: Denzel Bowles. Bowles barely missed the ORTG requirement (95.8). I think his defensive contributions are huge to San Mig Coffee so I included him into the import group. This means 5 imports were in the running for me and 6 locals.

Last note, this is not meant to be an “end-all, be-all” discussion as to who the most valuable import or players are. There can be a lot of discussion on the value of each “category.”

Here are the numbers for the Best Import and Best Player of the Conference (for the sake of discussion, I’m publishing my “defense + intangibles” score):

Name

MVP Score

Defense + Intangible

Robert Dozier

89.07

9

Vernon Macklin

83.16

9.5

Eric Dawson

89.61

10

Bruno Sundov

82.68

8

Denzel Bowles

82.33

10

Meralco Bolt’s Eric Dawson (Photo Credit: Interaksyon.com)

Explanation: Dozier gets a 9 because he was clearly the anchor of the team’s defense. He wasn’t the leader, by any means, but being the anchor of the top defense in the league should hold merit. Macklin gets a 9.5 because he’s also the anchor of the defense and shares co-leader responsibilities with Tenorio. Dawson gets a perfect 10 because he’s the anchor of the defense and the leader of the group. Sundov was neither the anchor on D nor the leader but he did play good defense in the post and in general. He gets an 8. Bowles, like Dawson, gets a 10 because he was both the leader and the anchor of the team.

Name

MVP Score

Defense + Intangible

LA Tenorio

76.61

9.5

Alex Cabagnot

72.39

8

Marcio Lassiter

75.67

8

PJ Simon

72.46

7.5

Jimmy Alapag

76.79

8.5

Larry Fonacier

68.75

8

Talk n Text’s Jimmy Alapag (Photo Credit: Yahoo! Sports)

Explanation: LA gets a 9.5 because he’s a good defender and he’s the main leader of the group (after Caguioa went out and sharing co-leader duties with Macklin). Cabagnot gets an 8 because (like it or not) he was the leader of the group when he played. He didn’t play good enough on defense but he was adequate. Lassiter was given an 8 because of his defense. He wasn’t the leader in general but he was an important piece of the puzzle defensively. PJ Simon gets a 7.5 because he was neither a leader nor played “good” defense (he did play adequately, most of the time). Alapag gets an 8.5 because he was the heart and soul of that TNT team. He doesn’t play as good enough on D as LA but he does carry more responsibility than Cabagnot (hence the 0.5 difference). Larry, like Lassiter, was given an 8 because of his defense.

Discussion and Final Notes

Should the discussion only cover the regular conference or should it include the playoffs (maybe until the semis)? Is this a regular conference award?

My candidates and explanation can be found on GMA News Online (Spoiler Alert: It’s Dawson and Alapag).

Dozier had the highest on/off court rating with -6.1 (i.e. Alaska was 6.1 points worse when Dozier was off the court). For comparison, 6.1 points is the difference between Rain or Shine’s league leading offense (101.1 points per 100-possession) and the 2nd worst offense in the league, the A21 Express (94.81 points per 100-possession).

Among the locals, the players with the lowest assisted percentage is Cabagnot, Tenorio and Alapag. That’s a very obvious answer since all 3 handle the ball a LOT.

Also, among the imports, Macklin had the lowest assisted percentage followed by Denzel Bowles.

Despite Cabagnot’s 104.4 offensive rating, Petron was 2.1 points better on offense when Cabagnot was OFF the court. Similarly, Petron was 2.6 points better with Lassiter off the court (despite his 104.3 offensive rating).

Among the locals, only Tenorio had a good on/off (i.e. Ginebra was better with him on the court). Alapag’s numbers were close to good (TNT was 0.5 points worse with him on the court). The rest didn’t fare well.